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CS 260 Winter 2014 Eamonn Keogh’s Presentation of

CS 260 Winter 2014 Eamonn Keogh’s Presentation of Thanawin Rakthanmanon , Bilson Campana , Abdullah Mueen , Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria , Eamonn Keogh (2012).  Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping  SIGKDD 2012

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CS 260 Winter 2014 Eamonn Keogh’s Presentation of

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  1. CS 260 Winter 2014 Eamonn Keogh’s Presentation of ThanawinRakthanmanon, BilsonCampana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, JesinZakaria, Eamonn Keogh (2012). Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping SIGKDD 2012 Slides I created for this 260 class have this green background

  2. Shooting Hand moving to shoulder level 1 Hand moving down to grasp gun Hand moving above holster Hand at rest 0.5 0 10 20 30 40 50 60 70 80 90 0 0 50 100 150 200 250 300 350 400 450 ? Lance Armstrong 400 200 0 2000 2001 2002 What is Time Series?

  3. What is Similarity Search I? Where is the closest match to Q in T? Q T

  4. What is Similarity Search II? Where is the closest match to Q in T? Q T

  5. What is Similarity Search II? Note that we must normalize the data Q T

  6. What is Indexing I? Indexing refers to any technique to search a collection of items, without having to examine every object. Obvious example: Search by last name Let look for Poe…. A-B-C-D-E-F G-H-I-J-K-L-M N-O-P-Q-R-S T-U-V-W-X-Y-Z

  7. What is Indexing II? It is possible to index almost anything, using Spatial Access Methods (SAMs) Q T

  8. What is Indexing II? It is possible to index almost anything, using Spatial Access Methods (SAMs)

  9. What is Dynamic Time Warping? Lowland Gorilla Gorilla gorilla graueri DTW Alignment Mountain Gorilla Gorilla gorilla beringei

  10. Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping Thanawin (Art) Rakthanmanon, BilsonCampana, Abdullah Mueen, Gustavo Batista, Qiang Zhu, Brandon Westover, JesinZakaria, Eamonn Keogh

  11. What is a Trillion? A trillion is simply one million million. Up to 2011 there have been 1,709 papers in this conference. If every such paper was on time series, and each had looked at five hundred million objects, this would still not add up to the size of the data we consider here. However, the largest time series data considered in a SIGKDD paper was a “mere” one hundred million objects.

  12. Dynamic Time Warping Similar but out of phase peaks. Q C C Q Q C R (Warping Windows)

  13. Motivation Similarity search is the bottleneck for most time series data mining algorithms. The difficulty of scaling search to large datasets explains why most academic work considered at few millions of time series objects.

  14. Objective Search and mine really big time series. Allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable.

  15. Assumptions (1) B C A • Time Series Subsequences must be Z-Normalized • In order to make meaningful comparisons between two time series, both must be normalized. • Offsetinvariance. • Scale/Amplitude invariance. • Dynamic Time Warping is the Best Measure (for almost everything) • Recent empirical evidence strongly suggests that none of the published alternatives routinely beats DTW.

  16. Assumptions (2) • Arbitrary Query Lengths cannot be Indexed • If we are interested in tackling a trillion data objects we clearly cannot fit even a small footprint index in the main memory, much less the much larger index suggested for arbitrary length queries. • There Exists Data Mining Problems that we are Willing to Wait Some Hours to Answer • a team of entomologists has spent three years gathering 0.2 trillion datapoints • astronomers have spent billions dollars to launch a satellite to collect one trillion datapoints of star-light curve data per day • a hospital charges $34,000 for a daylong EEG session to collect 0.3 trillion datapoints

  17. Proposed Method: UCR Suite • An algorithm for searching nearest neighbor • Support both ED and DTW search • Combination of various optimizations • Known Optimizations • New Optimizations

  18. C U L Q Known Optimizations (1) 2 LB_Keogh • Using the Squared Distance • Exploiting Multicores • More cores, more speed • Lower Bounding • LB_Yi • LB_Kim • LB_Keogh

  19. Known Optimizations (2) U, L is an envelope of Q Early Abandoning of ED Early Abandoning of LB_Keogh

  20. Known Optimizations (3) Stop ifdtw_dist ≥ bsf C Q dtw_dist R (Warping Windows) Early Abandoning of DTW Earlier Early Abandoning of DTW using LB Keogh

  21. Known Optimizations (3) Stop ifdtw_dist+lb_keogh ≥ bsf C Q (partial) lb_keogh (partial) dtw_dist R (Warping Windows) Early Abandoning of DTW Earlier Early Abandoning of DTWusing LB_Keogh

  22. UCR Suite • Known Optimizations • Early Abandoning of ED • Early Abandoning of LB_Keogh • Early Abandoning of DTW • Multicores New Optimizations

  23. UCR Suite: New Optimizations (1) • Early Abandoning Z-Normalization • Do normalization only when needed (just in time). • Small but non-trivial. • This step can break O(n) time complexity for ED (and, as we shall see, DTW). • Online mean and std calculation is needed.

  24. UCR Suite: New Optimizations (2) Idea - Order by the absolute height of the query point. - This step only can save about 30%-50% of calculations. • Reordering Early Abandoning • We don’t have to compute ED or LB from left to right. • Order points by expected contribution.

  25. UCR Suite: New Optimizations (3) • ------------------- • Online envelope calculation. Envelop on Q Envelop on C • Reversing the Query/Data Role in LB_Keogh • Make LB_Keogh tighter. • Much cheaper than DTW. • Triple the data.

  26. UCR Suite: New Optimizations (4) Tightness of LB (LB/DTW) • Cascading Lower Bounds • At least 18 lower bounds of DTW was proposed. • Use some lower bounds only on the Skyline.

  27. UCR Suite • Known Optimizations • Early Abandoning of ED • Early Abandoning of LB_Keogh • Early Abandoning of DTW • Multicores New Optimizations • Just-in-time Z-normalizations • Reordering Early Abandoning • Reversing LB_Keogh • Cascading Lower Bounds

  28. UCR Suite State-of-the-art* • Known Optimizations • Early Abandoning of ED • Early Abandoning of LB_Keogh • Early Abandoning of DTW • Multicores • *We implemented the State-of-the-art (SOTA) as well as we could. • SOTA is simplythe UCR Suite without new optimizations. New Optimizations • Just-in-time Z-normalizations • Reordering Early Abandoning • Reversing LB_Keogh • Cascading Lower Bounds

  29. Experimental Result: Random Walk • Random Walk: Varying size of the data Code and data is available at: www.cs.ucr.edu/~eamonn/UCRsuite.html

  30. Experimental Result: Random Walk Random Walk: Varying size of the query

  31. Experimental Result: DNA Query: Human Chromosome 2 of length 72,500 bps Data: Chimp Genome 2.9 billion bps Time: UCR Suite 14.6 hours, SOTA 34.6 days (830 hours)

  32. Experimental Result: EEG Data: 0.3 trillion points of brain wave Query: Prototypical Epileptic Spike of 7,000 points (2.3 seconds) Time: UCR-ED 3.4 hours, SOTA-ED 20.6 days (~500 hours)

  33. Experimental Result: ECG PVC(aka. skipped beat) ~30,000X faster than real time! Data: One year of Electrocardiograms 8.5 billion data points. Query: Idealized Premature Ventricular Contraction (PVC) of length 421(R=21=5%).

  34. Speeding Up Existing Algorithm • Time Series Shapelets: • SOTA 18.9 minutes, UCR Suite 12.5 minutes • Online Time Series Motifs: • SOTA 436 seconds, UCR Suite 156 seconds • Classification of Historical Musical Scores: • SOTA 142.4 hours, UCR Suite 720 minutes • Classification of Ancient Coins: • SOTA 12.8 seconds , UCR Suite 0.8 seconds • Clustering of Star Light Curves: • SOTA 24.8 hours, UCR Suite 2.2 hours

  35. Conclusion UCR Suite … is an ultra-fast algorithm for finding nearest neighbor. is the first algorithm that exactly mines trillion real-valued objects in a day or two with a "off-the-shelf machine". uses a combination of various optimizations. can be used as a subroutine to speed up other algorithms. Probably close to optimal ;-)

  36. Authors’ Photo  • Abdullah Mueen ThanawinRakthanmanon • BilsonCampana Gustavo Batista Brandon Westover JesinZakaria Eamonn Keogh Qiang Zhu

  37. Acknowledgements NSF grants 0803410 and 0808770 FAPESP award 2009/06349-0 Royal Thai Government Scholarship

  38. Papers Impact It was best paper winner at SIGKDD 2012 It has 37 references according to Google Scholar. Given that it has been in print only 18 months, this would make it among the most cited papers of that conference, that year. The work was expanded to a journal paper, which adds a section on uniform scaling.

  39. Discussion The paper made use of videos http://www.youtube.com/watch?v=c7xz9pVr05Q

  40. Questions About the paper? About the presentation of it?

  41. Backup Slides

  42. C U L Q LB_Keogh C Q Ui = max(qi-r : qi+r) Li = min(qi-r : qi+r) R (Warping Windows)

  43. C U C max(Q) A D L Q min(Q) B Known Optimizations • Lower Bounding • LB_Yi • LB_Kim • LB_Keogh

  44. Ordering This step only can save about 50% of calculations

  45. UCR Suite • New Optimizations • Just-in-time Z-normalizations • Reordering Early Abandoning • Reversing LB_Keogh • Cascading Lower Bounds • Known Optimizations • Early Abandoning of ED/LB_Keogh/DTW • Use Square Distance • Multicores

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